Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma
Abstract Accurate assessment of lymph node metastasis (LNM) in T1/2-stage esophageal squamous cell carcinoma (ESCC) is critical for treatment planning but remains challenging due to diagnostic inaccuracies and unclear metastatic mechanisms. This study aimed to predict LNM in T1/2-stage ESCC using ma...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-00929-2 |
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| author | Yu Zhang Long Liu Mengyu Han Linrui Li Qibing Wu Xin Wang |
| author_facet | Yu Zhang Long Liu Mengyu Han Linrui Li Qibing Wu Xin Wang |
| author_sort | Yu Zhang |
| collection | DOAJ |
| description | Abstract Accurate assessment of lymph node metastasis (LNM) in T1/2-stage esophageal squamous cell carcinoma (ESCC) is critical for treatment planning but remains challenging due to diagnostic inaccuracies and unclear metastatic mechanisms. This study aimed to predict LNM in T1/2-stage ESCC using machine learning-based radiomics and elucidate its biological underpinnings. We retrospectively analyzed 374 surgically treated ESCC patients from two centers, employing six machine-learning algorithms to derive an optimal radiomics score. Key pathways and genes linked to LNM were investigated via bioinformatics and experimental validation. The decision tree (DT)-based radiomics model demonstrated superior predictive performance, with AUCs of 0.933 (training), 0.887 (validation), and 0.845 (test). Bioinformatics analysis implicated tumor-lymphatic invasion pathways, with EFNA1 emerging as a potential key regulator. These findings highlight the clinical utility of radiomics for LNM prediction in early-stage ESCC and provide insights into its molecular mechanisms. |
| format | Article |
| id | doaj-art-eac0e500fc474abb92a4a99a5161949e |
| institution | OA Journals |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-eac0e500fc474abb92a4a99a5161949e2025-08-20T02:36:50ZengNature Portfolionpj Precision Oncology2397-768X2025-06-01911910.1038/s41698-025-00929-2Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinomaYu Zhang0Long Liu1Mengyu Han2Linrui Li3Qibing Wu4Xin Wang5Department of Radiation Therapy, The First Affiliated Hospital of Anhui Medical UniversityDepartment of Hepatobiliary and Pancreatic Surgery, The Second Hospital of Zhejiang UniversityDepartment of Radiation Therapy, The First Affiliated Hospital of Anhui Medical UniversityDepartment of Radiation Therapy, The First Affiliated Hospital of Anhui Medical UniversityDepartment of Radiation Therapy, The First Affiliated Hospital of Anhui Medical UniversityDepartment of Radiation Therapy, The First Affiliated Hospital of Anhui Medical UniversityAbstract Accurate assessment of lymph node metastasis (LNM) in T1/2-stage esophageal squamous cell carcinoma (ESCC) is critical for treatment planning but remains challenging due to diagnostic inaccuracies and unclear metastatic mechanisms. This study aimed to predict LNM in T1/2-stage ESCC using machine learning-based radiomics and elucidate its biological underpinnings. We retrospectively analyzed 374 surgically treated ESCC patients from two centers, employing six machine-learning algorithms to derive an optimal radiomics score. Key pathways and genes linked to LNM were investigated via bioinformatics and experimental validation. The decision tree (DT)-based radiomics model demonstrated superior predictive performance, with AUCs of 0.933 (training), 0.887 (validation), and 0.845 (test). Bioinformatics analysis implicated tumor-lymphatic invasion pathways, with EFNA1 emerging as a potential key regulator. These findings highlight the clinical utility of radiomics for LNM prediction in early-stage ESCC and provide insights into its molecular mechanisms.https://doi.org/10.1038/s41698-025-00929-2 |
| spellingShingle | Yu Zhang Long Liu Mengyu Han Linrui Li Qibing Wu Xin Wang Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma npj Precision Oncology |
| title | Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma |
| title_full | Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma |
| title_fullStr | Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma |
| title_full_unstemmed | Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma |
| title_short | Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma |
| title_sort | unraveling the power of radiomics prediction and exploration of lymph node metastasis in stage t1 2 esophageal squamous cell carcinoma |
| url | https://doi.org/10.1038/s41698-025-00929-2 |
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